Learning to Synchronize with Attention Models

Synchronization is often one of the most involved tasks to get right when building, testing, and deploying a radio system.  In this work, we look at treating synchronization as a learned attention model in a deep neural network to provide a canonical form signal for classification.  We use the same discriminative network as used in prior work and obtain slightly better classification performance.  We introduce a handful of new layers into Keras to build a domain specific set of radio transforms to supplement those used in imagery and described in this paper on Spatial Transformer Networks.


Classification is perhaps not the most interesting task to apply an attention model for synchronization.  Due to the extremely low SNR of much of the data-set, good synchronization is hard to achieve on short data samples with learned or expert synchronization metrics, and many of the learned discriminative features seem to be relatively robust to synchronization error.  We plan to revisit this attention model more in future work, potentially for other sorts of tasks for which it may be more beneficial, regardless, plotting a color-coded distribution over the density of constellation points before an after the transform on the QPSK subset of the data-set, we can definitely see some qualitative improvements in orderly signal structure.


Checkout the paper on arXiv for more details!

Convolutional Radio Modulation Recognition Networks

In an arxiv pre-publication report out today, Johnathan Corgan and I study the adaptation of convolutional neural networks to the task of modulation recognition in wireless systems.   We use a relatively simple two layer convolutional network followed by two dense layers, a much smaller network than required for tasks such as ImageNet/ILVC.


We demonstrate that blind time domain feature learning can perform extremely well at the task of modulation classification, achieving a very high accuracy rate on both clean and noisy data sets.


As we compare the classifier performance across a wide range of signal to noise ratios, we demonstrate that it outperforms a number of more traditional expert classifiers using zero-delay cumulant features by a large margin.


While this is preliminary work, we think the results are exciting and that many additional promising results will come from the marriage of software radio and deep learning fields.

For much more detail on these results, please see our paper!  http://arxiv.org/abs/1602.04105